Neuro-Snake Pattern Recognition And Classification Using Gradiant Vector Flow (Gvf And Hnn)

Abstract

The most popular applications of Hopfield neural network algorithm (HNN) arepattern recognition and classification. But the HNN has some limitation like the localminima (oscillation) problem. In this paper a novel method of combining an activecontour (snake) and an artificial neural network to behave together as pattern recognitionand classification is presented. The approach used the technique of the gradient vectorflow (GVF) that locate the boundary of target pattern (image) then pass it to a classifierbuilt by Hopfield algorithm to classify it according to one of the storage pattern. Thesnakes can find the boundaries of objects so it is very accurate to take the shape of theobject wanted, that will eliminate the noise from the original image and reduce the biterror rate of the Hopfield network to 0.215 and overcome the oscillation state inrecognition of the entered pattern. MATLAB 7 program have been used for thesimulation of the active contour and the pattern classification.